Let be the matrix unit with its only nonzero entry in row , column . For
one has
Therefore
The matrix units form an eigenbasis, with having eigenvalue . The zero eigenspace consists exactly of the diagonal matrices and has dimension . The rank-nullity theorem now gives
For
direct multiplication gives
With respect to the ordered standard basis , its matrix is therefore
This nilpotent operator satisfies , while . Moreover,
These ranks determine one nilpotent block of size three and one of size one. Hence its Jordan normal form is
The quotient topology on is
Since inverse images preserve arbitrary unions and finite intersections,
Also and . The displayed collection therefore satisfies all the topology axioms.
If is continuous, then is continuous because the quotient map is continuous by definition.
Conversely, suppose is continuous. For every open set ,
is open in . The definition of the quotient topology then says that is open in . Hence
This is the universal property of the quotient topology.
No. Take the Hausdorff space and declare all nonzero points equivalent, leaving in its own class. The quotient has two points,
The nonzero class is open because its inverse image is open. However, the only saturated set containing but not the other class is , which is not open. Thus every neighbourhood of is the whole quotient, so the two quotient points cannot have disjoint neighbourhoods. Therefore need not be Hausdorff.
The hypothesis says that has a zero of order at , so write
with holomorphic near . Differentiating gives
Choose so small that the closed disc lies in the domain, never vanishes there, and the parenthesized factor never vanishes there. Thus is the only critical point of in that disc.
On the boundary circle, is nonzero. Set
If , then on the boundary. Rouché's theorem applied to and says that has the same number of zeros in as , namely , counted with multiplicity.
None of these zeros is because , and none is a critical point because has no other zero in the disc. Every zero of is therefore simple. Hence there are exactly
The one-dimensional Time-dependent Schrodinger equation for a wavefunction in a real potential is
Multiplying this equation by , multiplying its complex conjugate by , and subtracting gives the probability continuity equation
If and its first derivative decay sufficiently rapidly as , then the probability current vanishes at both ends. Integrating the continuity equation and using the fundamental theorem of calculus yields
This conservation of quantum probability is required by the Born rule: once a normalizable wavefunction has total probability one, its time evolution must preserve that normalization.
For the stated Gaussian wave packet, put . Since
its squared modulus is
The Gaussian integral then gives
which is independent of time, as required.
In a vacuum with no charge or current, the Maxwell equations are
For the proposed plane electromagnetic wave, gives
The Ampère-Maxwell equation gives
and substituting this into Faraday's law gives the dispersion relation
Thus both fields have transverse polarization relative to , and the corresponding real electric field is
For incidence in the positive -direction, choose the incident fields
The perfect conductor requires the tangential electric field to vanish at . The reflected wave therefore has
This is normal reflection of an electromagnetic wave from a perfect conductor. The magnetic field in is
so its tangential value at the surface is
Use the nodes in the order . Their first divided differences are , their second divided differences are , and their third divided difference is . The Newton interpolation polynomial is therefore
This is the unique interpolating polynomial of degree at most three through the four data points.
Appending the node to the existing Newton interpolation polynomial gives
At the new node,
The condition therefore gives
Hence
For the simple random walk on the integer line, a return to the origin is possible only at an even time. At time , exactly of the increments must be , so the return probability is
The supplied factorial bounds, equivalently the order estimate in the Stirling formula, give
Consequently diverges. By the recurrence criterion by return probabilities, the origin is a recurrent state; translation invariance then makes the whole walk recurrent.
For three independent walks, the probability that all three are at the origin at time is
This P-series is convergent, so the first of the Borel-Cantelli lemmas says that simultaneous returns occur only finitely often with probability one. Therefore the requested probability is
A Hermitian form is a sesquilinear form such that
Use the convention that is linear in its first argument and conjugate-linear in its second. Its matrix in the basis is
If and , then
Since , the coordinate row of each new basis vector is a row of . Direct substitution gives
so the new matrix is
The subspace is the radical of . In coordinates it is the kernel of , and the rank-nullity theorem gives
The symmetric matrix of the quadratic form in the standard basis is
because the off-diagonal entries contribute twice to .
Apply Gram-Schmidt orthogonalization for a symmetric bilinear form to
These vectors are pairwise orthogonal for , and
Thus the form is diagonal in the basis , with matrix
It follows that its rank is and its signature is
By the real spectral theorem, the real symmetric matrix has an orthonormal basis of eigenvectors. In that basis the associated quadratic form is
Rescaling the coordinates belonging to nonzero eigenvalues changes every positive coefficient to and every negative coefficient to . Hence the diagonal normal form has one positive square for each positive eigenvalue and one negative square for each negative eigenvalue. By Sylvester's law of inertia, these counts do not depend on the diagonalizing basis, so
The numerical eigenvalues are not invariant under a general change of basis, because the matrix changes by congruence rather than similarity. For example, on a one-dimensional space let . Its matrix in the basis is , whereas in the basis it is . The eigenvalue changes from to , although its sign, and therefore the signature, is unchanged.
For subgroups , define when . The identity elements show reflexivity, inverses show symmetry, and multiplication shows transitivity. Thus the double cosets partition .
Let act by left multiplication on the set of left cosets . The orbit of consists of the cosets contained in , so its size is . Its stabilizer is
The orbit-stabilizer theorem therefore gives
Suppose is a Sylow subgroup of , with , and write the largest power of dividing as . If every had order at most , the displayed formula would make every double-coset size divisible by . Their sum would then also be divisible by , contradicting the choice of . Hence some has order and is a Sylow -subgroup of . This is the Sylow subgroup of a subgroup from double cosets argument.
To count the general linear group over a finite field , choose its columns successively. There are choices for the first, for the second, and for column . Consequently
None of the factors is divisible by , so the upper unitriangular group is a Sylow -subgroup: it has one arbitrary field entry in each of the positions above the diagonal and hence order . The permutation matrices form a subgroup isomorphic to the symmetric group .
By Cayley theorem, every finite group embeds in , and permutation matrices embed this symmetric group in . The latter has the explicit Sylow -subgroup just described, so the result proved in the first part, applied to the embedded copy of , proves that every finite group has a Sylow -subgroup.
The counting part of the Sylow theorems says that if is the largest power of dividing , then the number of Sylow -subgroups satisfies
Finally, let with prime numbers . The Sylow counts give
so and the Sylow -subgroup is a normal subgroup. Also and . If , both Sylow subgroups are normal; their elements commute, so is their direct product and is abelian. In the nonabelian case one must therefore have , whence
or equivalently , as recorded by nonabelian group of order pq.
Fix and a coordinate direction . The fundamental theorem of calculus gives
For sufficiently small , the points lie in a fixed compact set. The continuity of makes it uniformly continuous there, so the right-hand side converges to uniformly in . We may consequently pass the limit through the finite integral:
To prove continuity, if , joint continuity makes uniformly for once the lie in a compact neighbourhood of . Hence . This proves the stated differentiation under the integral sign result and the continuity of every partial derivative.
For fixed , apply the given one-variable identity to the smooth function
The chain rule gives
Therefore
where
Every derivative of the integrand is continuous. Repeated differentiation under the integral sign therefore shows that is smooth. This is the Second-order Hadamard lemma.
An abstract smooth surface is a Hausdorff second-countable topological space with an atlas of charts to open subsets of whose transition maps are smooth. It is orientable when it has such an atlas for which every transition map has positive Jacobian determinant. A map is a smooth map when every coordinate representation
is smooth wherever it is defined.
The map is a smooth involution with no fixed point: the equation
would force , which is impossible on . For each , choose a sufficiently small coordinate neighbourhood such that
Then the quotient projection restricts to a homeomorphism
Transporting a smooth chart across this homeomorphism gives the quotient chart
On an overlap, a lift lies either in another chosen neighbourhood or in its image under . The corresponding transition map is therefore a transition map on , possibly composed with the diffeomorphism , and is smooth. Since this is a free action of the finite group , the quotient is Hausdorff and second countable. This constructs the smooth quotient by a free finite group action, and in these charts is locally the identity. Hence is a local diffeomorphism, in particular smooth.
To test orientability, parametrize the cylinder by
The involution acts in these coordinates as
whose derivative has determinant . Thus is an orientation-reversing diffeomorphism of the cylinder.
If the quotient surface were orientable, its orientation would pull back through the local diffeomorphism to an orientation of . The identity would then force to preserve that pulled-back orientation, contradicting the negative determinant above. Therefore
The Laplace transform of a function on is
for values of for which the integral converges. Integration by parts gives the Laplace transform of a derivative
Write . Transforming the first two equations and using the initial data gives
The third equation then gives
and hence
A partial fraction decomposition therefore yields the sequential radioactive decay formula
Now let and put . The transformed contribution originating from the initial population becomes
Equivalently, solve the middle equation first to obtain
and use an integrating factor in the final equation:
Evaluating the elementary integral gives
Let , where the variation satisfies . Differentiating the functional at gives its first variation
Using integration by parts and the fixed endpoint conditions,
If this vanishes for every admissible , the fundamental lemma of the calculus of variations gives the Euler-Lagrange equation
For
one has and
A circle of radius centred at on the -axis satisfies
On any arc that does not cross ,
which is constant. Hence , so the Euler-Lagrange equation holds and . These circles are the coordinate-swapped form of a Geodesic in the Poincare half-plane model.
Here the integrand
has no explicit -dependence. The Beltrami identity therefore gives
for a positive constant . Thus
whose nonvertical solutions are the semicircles
The endpoint and the prescribed value at both lie on
because
The required positive arc is consequently
The condition keeps the endpoint above the -axis. The sketch is the descending part of the upper semicircle of radius centred at , from to
This is precisely a Geodesic in the Poincare half-plane model.
For
direct differentiation gives
and
Thus .
Its total mass is found from the Gaussian integral:
The unit-mass solution converging to the Dirac delta function as is therefore the heat kernel
For the second initial condition, the heat-kernel solution is the convolution
With and the definition of the error function, this becomes
This is the heat equation with interval-indicator initial data for .
At the graph is the rectangle of height one on . For every it is smooth, positive and even, with its maximum at . As time increases the graph broadens and its maximum falls, while its total area remains ; pointwise it tends to zero as .
The orbital angular momentum commutation relations give
Consequently
The same commutation relations imply , and hence
These are the defining identities for the angular momentum ladder operators.
If
then, whenever ,
Thus has quantum numbers .
The Hamiltonian is the sum of three commuting one-dimensional harmonic-oscillator Hamiltonians. Its product eigenstates are
with energies
Equivalently, the level with has energy , as in the three-dimensional isotropic harmonic oscillator.
Up to normalization, the ground-state wavefunction is
It is radial, so is parallel to the position vector. Since the orbital angular momentum is , every component of annihilates this state. Therefore
and the ground state has .
The first excited level has and is spanned by
The state
satisfies . Applying the ladder operators gives
Hence convenient eigenstates are
up to normalization and irrelevant overall phases.
Finally, the isotropic Hamiltonian is rotationally invariant. The rotational invariance of a central-potential Hamiltonian gives
These commuting self-adjoint operators can be simultaneously diagonalized within each energy eigenspace, which is why joint eigenstates of must exist.
In spherical symmetry, the velocity is radial and constant over each sphere. Hence
Apply the divergence theorem to the fluid shell between radii and . Since the flow is incompressible,
Thus is independent of and is a function of time alone. This is the flux law for spherically symmetric incompressible radial flow.
The radial velocity and velocity potential are
Choose the gauge in the Unsteady Bernoulli equation by evaluating it at infinity, where , , and :
At the cavity surface the vacuum pressure is zero, and the kinematic boundary condition gives . Since
Bernoulli's equation becomes
Therefore
At the moving boundary,
Substitution into the result of part (b) gives the Rayleigh collapse equation
Treat as a function of , so that . With , the equation becomes
Multiplication by the integrating factor after division by gives
Using ,
The collapsing branch has negative radial velocity, so
Since on the collapsing branch, the time for to decrease from to zero is
Equivalently, scaling gives
Here has the binomial distribution , so, up to a factor independent of , the likelihood function is
For , differentiating the log-likelihood gives
and the boundary cases give the same formula. Thus the binomial proportion maximum-likelihood estimator is
The estimator is unbiased because , and
The bias-variance decomposition of mean squared error therefore gives
The quadratic is maximized at , so
The uniform prior is the Beta distribution . By Beta-binomial conjugacy, after observing heads the posterior is
The Bayes estimator under squared error loss is the posterior mean, hence
For the weighted loss, the Bayes estimator under parameter-weighted squared error is the mean of the posterior after multiplication by
Since the original posterior density is proportional to
the reweighted density is . Its mean gives
Writing this in the required form,
For
the bias and variance are
The bias-variance decomposition of mean squared error yields
This is the risk formula for an affine shrinkage estimator for a binomial proportion.
Set and . Since
the risk is affine in :
Its supremum is therefore controlled by the midpoint and the endpoint limit . Requiring both endpoint values to be at most the MLE's maximal risk gives
Equivalently,
Their intersection is
For precisely this range, the shrinkage estimator has maximal mean squared error no greater than that of .
A vector is a basic feasible solution when
and the columns for which are linearly independent. Equivalently, after extending those columns to a basis of the column space of , one sets all nonbasic variables to zero and solves for the basic variables, obtaining a nonnegative vector.
Choose an optimal feasible vector having as few positive coordinates as possible, and let
If the columns were linearly dependent, there would be a nonzero vector , supported on , such that . For all sufficiently small positive , both
would remain feasible.
If , one of these two perturbations would increase the objective, contradicting optimality. Hence . We may then increase in one of the two directions until at least one positive coordinate first becomes zero. The resulting vector is still feasible and optimal but has smaller positive support, contradicting the choice of .
Thus the active columns are linearly independent, so is basic. This proves the Fundamental theorem of linear programming: whenever the finite maximum is attained, an optimal basic feasible solution exists.
Apply the Charnes-Cooper transformation to any feasible point of :
Then
so is feasible for , and
In particular, the given solution of produces a feasible point of with the same objective value, so
Moreover, because every , the equation implies
The linear objective is therefore bounded on the feasible set. The feasible lie in the compact simplex . Their subset arising in is closed: if , any nonzero component of determines continuously from , while if the condition is simply . Hence the feasible form a compact set, on which the continuous objective attains a finite maximum. Thus has a finite maximum at least as large as that of .
Let be feasible for . Since and , the vector is nonzero. Every entry of is strictly positive and , so every component of is strictly positive. The relation
therefore forces .
Set . Then
so is feasible for , and
Thus every feasible value of is a feasible value of the linear-fractional program . Together with part (i), this proves
Let be a basic feasible solution of . Part (ii) gives , and put . We already know that is feasible for .
Suppose the columns with were linearly dependent. Then some nonzero , supported on those indices, would satisfy . Put
Since and ,
The vector is nonzero and is supported only on positive variables of . It is therefore a linear dependence among the active constraint columns of , contradicting that is basic. Hence the active columns are linearly independent, and
By the Fundamental theorem of linear programming, has an optimal basic feasible solution. Its image has the same objective ratio, by part (ii). Conversely, every is feasible for and maps to a feasible point of . Since solves ,

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